{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "generate-stroke-examples.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reiinakano/diff-painter/blob/master/notebooks/generate_stroke_examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "9P5f_uzAq_5g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install packages"
      ]
    },
    {
      "metadata": {
        "id": "LRItSKP3Kqri",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Optionally connect to Drive for saving"
      ]
    },
    {
      "metadata": {
        "id": "cl2EXKyLl06w",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sw1EJdAmKuuj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Install MyPaint"
      ]
    },
    {
      "metadata": {
        "id": "bAWyePtkn3av",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Install mypaint\n",
        "!apt-get update\n",
        "!apt-get install libjson-c-dev libgirepository1.0-dev libglib2.0-dev\n",
        "!apt-get install autotools-dev intltool gettext libtool\n",
        "!apt-get install swig python-setuptools gettext g++\n",
        "!apt-get install -y libgtk-3-dev python-gi-dev\n",
        "!apt-get install -y libpng-dev liblcms2-dev libjson-c-dev\n",
        "!apt-get install -y gir1.2-gtk-3.0 python-gi-cairo\n",
        "!apt-get install scons\n",
        "\n",
        "!wget https://github.com/mypaint/libmypaint/releases/download/v1.3.0/libmypaint-1.3.0.tar.xz\n",
        "!tar -xvf libmypaint-1.3.0.tar.xz\n",
        "!mv libmypaint-1.3.0 libmypaint\n",
        "\n",
        "!cd libmypaint && ./configure && make install\n",
        "\n",
        "!wget https://github.com/mypaint/mypaint/releases/download/v1.2.1/mypaint-1.2.1.tar.xz\n",
        "!tar -xvf mypaint-1.2.1.tar.xz\n",
        "!mv mypaint-1.2.1 mypaint\n",
        "!cd mypaint && scons && scons install\n",
        "\n",
        "!ldconfig\n",
        "\n",
        "!pip install ipdb tqdm pathlib cloudpickle future-fstrings matplotlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zxiqcngXKxb0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Fetch a package for using the MyPaint environment"
      ]
    },
    {
      "metadata": {
        "id": "1ueV31ATgbQp",
        "colab_type": "code",
        "outputId": "a05cde40-0aa3-4729-865b-edf4d4db283e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/reiinakano/SPIRAL-tensorflow.git\n",
        "!cd SPIRAL-tensorflow && git checkout reiinakano-patch-2  #reiinakano-patches"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'SPIRAL-tensorflow'...\n",
            "remote: Enumerating objects: 8, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 154 (delta 3), reused 0 (delta 0), pack-reused 146\u001b[K\n",
            "Receiving objects: 100% (154/154), 1.36 MiB | 2.91 MiB/s, done.\n",
            "Resolving deltas: 100% (75/75), done.\n",
            "Branch 'reiinakano-patch-2' set up to track remote branch 'reiinakano-patch-2' from 'origin'.\n",
            "Switched to a new branch 'reiinakano-patch-2'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a1UgoHsHK9Cp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports"
      ]
    },
    {
      "metadata": {
        "id": "3ETScoLEgPJ-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import os\n",
        "from pathlib import Path\n",
        "\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "\n",
        "sys.path.append('mypaint')\n",
        "sys.path.append('SPIRAL-tensorflow')\n",
        "#import envs.mnist\n",
        "\n",
        "from tqdm import tqdm\n",
        "import tensorflow as tf\n",
        "from PIL import Image, ImageDraw\n",
        "from collections import defaultdict\n",
        "\n",
        "from lib import surface, tiledsurface, brush\n",
        "from envs.mypaint_utils import *\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vRB5EmewH_x9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bIiG_vM1K-6W",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Set up paint environment"
      ]
    },
    {
      "metadata": {
        "id": "asFw-F-ghKAU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class args:\n",
        "  jump=True\n",
        "  curve=True\n",
        "  screen_size=64\n",
        "  location_size=32\n",
        "  color_channel=3\n",
        "  brush_path='SPIRAL-tensorflow/assets/brushes/dry_brush.myb'\n",
        "  data_dir=Path('data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jT30XmogTskB",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class PaintMode:\n",
        "  STROKES_ONLY = 0\n",
        "  JUMP_STROKES = 1\n",
        "  CONNECTED_STROKES = 2\n",
        "\n",
        "class ColorEnv():\n",
        "    head = 0.25\n",
        "    tail = 0.75\n",
        "    \n",
        "    # all 0 to 1\n",
        "    actions_to_idx = {\n",
        "        'pressure': 0,\n",
        "        'size': 1,\n",
        "        'control_x': 2,\n",
        "        'control_y': 3,\n",
        "        'end_x': 4,\n",
        "        'end_y': 5,\n",
        "        'color_r': 6,\n",
        "        'color_g': 7,\n",
        "        'color_b': 8,\n",
        "        'start_x': 9,\n",
        "        'start_y': 10,\n",
        "        'entry_pressure': 11,\n",
        "    }\n",
        "\n",
        "    def __init__(self, args, paint_mode=PaintMode.JUMP_STROKES):\n",
        "        self.args = args\n",
        "        self.paint_mode = paint_mode\n",
        "\n",
        "        # screen\n",
        "        self.screen_size = args.screen_size\n",
        "        self.height, self.width = self.screen_size, self.screen_size\n",
        "        self.observation_shape = [\n",
        "                self.height, self.width, args.color_channel]\n",
        "\n",
        "        # location\n",
        "        self.location_size = args.location_size\n",
        "        self.location_shape = [self.location_size, self.location_size]\n",
        "        \n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "    \n",
        "    @staticmethod\n",
        "    def pretty_print_action(ac):\n",
        "        for k, v in ColorEnv.actions_to_idx.items():\n",
        "            print(k, ac[v])\n",
        "    \n",
        "    def random_action(self):\n",
        "        return np.random.uniform(size=[len(self.actions_to_idx)])\n",
        "      \n",
        "    def reset(self):\n",
        "        self.intermediate_images = []\n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "\n",
        "        self.s = tiledsurface.Surface()\n",
        "        self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "        with open(self.args.brush_path) as fp:\n",
        "            self.bi = brush.BrushInfo(fp.read())\n",
        "        self.b = brush.Brush(self.bi)\n",
        "\n",
        "    def draw(self, ac, s=None, dtime=1):\n",
        "        # Just added this\n",
        "        if self.paint_mode == PaintMode.STROKES_ONLY:\n",
        "          self.s.clear()\n",
        "          self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "          self.s.end_atomic()\n",
        "          self.s.begin_atomic()\n",
        "        \n",
        "        if s is None:\n",
        "            s = self.s\n",
        "\n",
        "        s_x, s_y = ac[self.actions_to_idx['start_x']]*64, ac[self.actions_to_idx['start_y']]*64  \n",
        "        e_x, e_y = ac[self.actions_to_idx['end_x']]*64, ac[self.actions_to_idx['end_y']]*64\n",
        "        c_x, c_y = ac[self.actions_to_idx['control_x']]*64, ac[self.actions_to_idx['control_y']]*64\n",
        "        color = (\n",
        "            ac[self.actions_to_idx['color_r']],\n",
        "            ac[self.actions_to_idx['color_g']],\n",
        "            ac[self.actions_to_idx['color_b']],\n",
        "        )\n",
        "        pressure = ac[self.actions_to_idx['pressure']]*0.8\n",
        "        entry_pressure = ac[self.actions_to_idx['entry_pressure']]*0.8\n",
        "        size = ac[self.actions_to_idx['size']] * 2.\n",
        "        \n",
        "        if self.paint_mode == PaintMode.CONNECTED_STROKES:\n",
        "            if self.prev_x is not None:\n",
        "                s_x, s_y, entry_pressure = self.prev_x, self.prev_y, self.prev_pressure\n",
        "            self.prev_x, self.prev_y, self.prev_pressure = e_x, e_y, pressure\n",
        "\n",
        "        self.b.brushinfo.set_color_rgb(color)\n",
        "        \n",
        "        self.b.brushinfo.set_base_value('radius_logarithmic', size)\n",
        "\n",
        "        # Move brush to starting point without leaving it on the canvas.\n",
        "        self._stroke_to(s_x, s_y, 0)\n",
        "\n",
        "        self._draw(s_x, s_y, e_x, e_y, c_x, c_y, entry_pressure, pressure, size, color, dtime)\n",
        "\n",
        "    def _draw(self, s_x, s_y, e_x, e_y, c_x, c_y,\n",
        "              entry_pressure, pressure, size, color, dtime):\n",
        "\n",
        "        # if straight line or jump\n",
        "        if pressure == 0:\n",
        "            self.b.stroke_to(\n",
        "                    self.s.backend, e_x, e_y, pressure, 0, 0, dtime)\n",
        "        else:\n",
        "            self.curve(c_x, c_y, s_x, s_y, e_x, e_y, entry_pressure, pressure)\n",
        "            \n",
        "        # Relieve brush pressure for next jump\n",
        "        self._stroke_to(e_x, e_y, 0)\n",
        "\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "    # sx, sy = starting point\n",
        "    # ex, ey = end point\n",
        "    # kx, ky = curve point from last line\n",
        "    # lx, ly = last point from InteractionMode update\n",
        "    def curve(self, cx, cy, sx, sy, ex, ey, entry_pressure, pressure):\n",
        "        #entry_p, midpoint_p, junk, prange2, head, tail\n",
        "        entry_p, midpoint_p, prange1, prange2, h, t = \\\n",
        "                self._line_settings(entry_pressure, pressure)\n",
        "\n",
        "        points_in_curve = 100\n",
        "        mx, my = midpoint(sx, sy, ex, ey)\n",
        "        length, nx, ny = length_and_normal(mx, my, cx, cy)\n",
        "        cx, cy = multiply_add(mx, my, nx, ny, length*2)\n",
        "        x1, y1 = difference(sx, sy, cx, cy)\n",
        "        x2, y2 = difference(cx, cy, ex, ey)\n",
        "        head = points_in_curve * h\n",
        "        head_range = int(head)+1\n",
        "        tail = points_in_curve * t\n",
        "        tail_range = int(tail)+1\n",
        "        tail_length = points_in_curve - tail\n",
        "\n",
        "        # Beginning\n",
        "        px, py = point_on_curve_1(1, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "        length, nx, ny = length_and_normal(sx, sy, px, py)\n",
        "        bx, by = multiply_add(sx, sy, nx, ny, 0.25)\n",
        "        self._stroke_to(bx, by, entry_p)\n",
        "        pressure = abs(1/head * prange1 + entry_p)\n",
        "        self._stroke_to(px, py, pressure)\n",
        "\n",
        "        for i in xrange(2, head_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs(i/head * prange1 + entry_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        # Middle\n",
        "        for i in xrange(head_range, tail_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            self._stroke_to(px, py, midpoint_p)\n",
        "\n",
        "        # End\n",
        "        for i in xrange(tail_range, points_in_curve+1):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs((i-tail)/tail_length * prange2 + midpoint_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        return pressure\n",
        "\n",
        "    def _stroke_to(self, x, y, pressure, duration=0.1):\n",
        "        self.b.stroke_to(\n",
        "                self.s.backend,\n",
        "                x, y,\n",
        "                pressure,\n",
        "                0.0, 0.0,\n",
        "                duration)\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "        self.intermediate_images.append(self.image)\n",
        "\n",
        "    def save_image(self, path=\"test.png\"):\n",
        "        Image.fromarray(self.image.astype(np.uint8).squeeze()).save(path)\n",
        "        #self.s.save_as_png(path, alpha=False)\n",
        "\n",
        "    @property\n",
        "    def image(self):\n",
        "        rect = [0, 0, self.height, self.width]\n",
        "        scanline_strips = \\\n",
        "                surface.scanline_strips_iter(self.s, rect)\n",
        "        return next(scanline_strips)\n",
        "\n",
        "    def _line_settings(self, entry_pressure, pressure):\n",
        "        p1 = entry_pressure\n",
        "        p2 = (entry_pressure + pressure) / 2\n",
        "        p3 = pressure\n",
        "        if self.head == 0.0001:\n",
        "            p1 = p2\n",
        "        prange1 = p2 - p1\n",
        "        prange2 = p3 - p2\n",
        "        return p1, p2, prange1, prange2, self.head, self.tail\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ukbt7gF41v-q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EACO9tboLCPr",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Sanity check paint environment"
      ]
    },
    {
      "metadata": {
        "id": "OZ5vCnNm1q7n",
        "colab_type": "code",
        "outputId": "319d1028-66e6-4ccb-f2eb-60e85e7c20ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2537
        }
      },
      "cell_type": "code",
      "source": [
        "env=ColorEnv(args, paint_mode=PaintMode.JUMP_STROKES)\n",
        "\n",
        "try:\n",
        "  os.makedirs('data/generated')\n",
        "except OSError:\n",
        "  pass\n",
        "\n",
        "for ep_idx in range(1):\n",
        "    env.reset()\n",
        "\n",
        "    for i in range(5):\n",
        "        action = env.random_action()\n",
        "        #action[env.actions_to_idx['color_r']] = 0.50\n",
        "        #action[env.actions_to_idx['color_g']] = 0.2\n",
        "        #action[env.actions_to_idx['color_b']] = 0.3\n",
        "        print(\"[Step {}] ac: {}\".format(i, action))\n",
        "        ColorEnv.pretty_print_action(action)\n",
        "        env.draw(action)\n",
        "        plt.imshow(env.image)\n",
        "        plt.show()\n",
        "        env.save_image(\"data/generated/mnist{}_{}.png\".format(ep_idx, i))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Step 0] ac: [0.26404815 0.96822001 0.15672115 0.82396459 0.55500448 0.25566826\n",
            " 0.02312342 0.9990442  0.50381175 0.529559   0.16601173 0.99508717]\n",
            "('end_y', 0.255668260693107)\n",
            "('end_x', 0.5550044837676973)\n",
            "('color_g', 0.9990441960515342)\n",
            "('color_b', 0.5038117495288398)\n",
            "('pressure', 0.2640481532670036)\n",
            "('entry_pressure', 0.9950871716775643)\n",
            "('color_r', 0.023123417389094736)\n",
            "('size', 0.9682200100059787)\n",
            "('control_y', 0.8239645860429625)\n",
            "('control_x', 0.1567211532692091)\n",
            "('start_x', 0.5295589957275088)\n",
            "('start_y', 0.1660117310655207)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GXHMTOJq1/PQ4ZpldI7N9Oz215ifY8Rh9lpqhUbCC4c/j9HQ+Fg5xJ9p4DdyaVwiVadH1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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 1] ac: [0.1023948  0.00936818 0.66990414 0.39761406 0.30366414 0.59681869\n",
            " 0.57223802 0.85706269 0.71255906 0.72911998 0.94880299 0.58340493]\n",
            "('end_y', 0.5968186902178001)\n",
            "('end_x', 0.30366414048940904)\n",
            "('color_g', 0.8570626923349071)\n",
            "('color_b', 0.7125590572008813)\n",
            "('pressure', 0.10239480379446841)\n",
            "('entry_pressure', 0.5834049287336872)\n",
            "('color_r', 0.5722380174290482)\n",
            "('size', 0.009368175149796154)\n",
            "('control_y', 0.39761405517650417)\n",
            "('control_x', 0.6699041443957969)\n",
            "('start_x', 0.7291199844344761)\n",
            "('start_y', 0.9488029948662642)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yV6ecmW5Yk/7PXnyHxSbb4DZ/H1knnzNlw61ezB6EPvqPy2vZZJvZSUv8U6jl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\nQQGJiR8EBSQmfhAUkJj4QVBA/j9bqXe92Gi4IQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 2] ac: [0.96587299 0.76489805 0.71957098 0.39055768 0.95974236 0.55350308\n",
            " 0.96092335 0.34436994 0.51391299 0.74046183 0.85389628 0.38754596]\n",
            "('end_y', 0.5535030804642671)\n",
            "('end_x', 0.9597423622339342)\n",
            "('color_g', 0.34436993670879856)\n",
            "('color_b', 0.5139129859386249)\n",
            "('pressure', 0.9658729929085194)\n",
            "('entry_pressure', 0.38754596201020397)\n",
            "('color_r', 0.9609233549809425)\n",
            "('size', 0.7648980535788686)\n",
            "('control_y', 0.3905576761852112)\n",
            "('control_x', 0.7195709799868061)\n",
            "('start_x', 0.7404618281675478)\n",
            "('start_y', 0.8538962786161334)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ggMSNHwQFZMCmvnbMeN9aup7RJguwYTQ22b2pzya8D9NdlcWe+62pM/XJ3M4L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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 3] ac: [0.3557302  0.00308079 0.12483841 0.82961693 0.25336996 0.64261501\n",
            " 0.9151078  0.3691103  0.97120005 0.38226892 0.27619563 0.64140084]\n",
            "('end_y', 0.6426150071525691)\n",
            "('end_x', 0.2533699593771812)\n",
            "('color_g', 0.36911030044049375)\n",
            "('color_b', 0.9712000480463424)\n",
            "('pressure', 0.35573020007108636)\n",
            "('entry_pressure', 0.6414008443789228)\n",
            "('color_r', 0.9151077964708547)\n",
            "('size', 0.003080790273704337)\n",
            "('control_y', 0.8296169346434754)\n",
            "('control_x', 0.12483841406868024)\n",
            "('start_x', 0.38226892287354897)\n",
            "('start_y', 0.27619562740168646)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Be61kPn1vGaXr4cx2rp7bEl3YXz+mTw5qh3jjBwIZREz8QCCDiIkfCGQQPfXx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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 4] ac: [0.82820981 0.51598894 0.37106164 0.72665257 0.61908719 0.57036894\n",
            " 0.26007435 0.80208871 0.35733639 0.5359044  0.93886554 0.39163037]\n",
            "('end_y', 0.5703689352742707)\n",
            "('end_x', 0.6190871866356761)\n",
            "('color_g', 0.8020887098317845)\n",
            "('color_b', 0.3573363885998133)\n",
            "('pressure', 0.8282098107086984)\n",
            "('entry_pressure', 0.3916303740012448)\n",
            "('color_r', 0.2600743518227354)\n",
            "('size', 0.5159889409836421)\n",
            "('control_y', 0.7266525695130116)\n",
            "('control_x', 0.37106163846648565)\n",
            "('start_x', 0.5359043961075507)\n",
            "('start_y', 0.9388655449682708)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p0/EHUqRdbUqPXaxY1YTdeyxSA4BQZJ3n0gNH8lGknfhSVuwSrNjflcxrtJ6M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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "y3_6GuaK1q5M",
        "colab_type": "code",
        "outputId": "e2f3a7b9-adc0-4ae7-d3e2-3b7ea0bbac79",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2537
        }
      },
      "cell_type": "code",
      "source": [
        "env=ColorEnv(args, paint_mode=PaintMode.STROKES_ONLY)\n",
        "\n",
        "try:\n",
        "  os.makedirs('data/generated')\n",
        "except OSError:\n",
        "  pass\n",
        "\n",
        "for ep_idx in range(1):\n",
        "    env.reset()\n",
        "\n",
        "    for i in range(5):\n",
        "        action = env.random_action()\n",
        "        #action[env.actions_to_idx['color_r']] = 0.50\n",
        "        #action[env.actions_to_idx['color_g']] = 0.2\n",
        "        #action[env.actions_to_idx['color_b']] = 0.3\n",
        "        print(\"[Step {}] ac: {}\".format(i, action))\n",
        "        ColorEnv.pretty_print_action(action)\n",
        "        env.draw(action)\n",
        "        plt.imshow(env.image)\n",
        "        plt.show()\n",
        "        env.save_image(\"data/generated/mnist{}_{}.png\".format(ep_idx, i))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Step 0] ac: [0.93273729 0.88879811 0.44361509 0.30820941 0.64586243 0.26765199\n",
            " 0.07082042 0.24761258 0.54161853 0.85851301 0.9840184  0.53020547]\n",
            "('end_y', 0.2676519890379069)\n",
            "('end_x', 0.6458624305453227)\n",
            "('color_g', 0.24761258061517588)\n",
            "('color_b', 0.5416185291241625)\n",
            "('pressure', 0.9327372928077287)\n",
            "('entry_pressure', 0.5302054734953655)\n",
            "('color_r', 0.0708204231802464)\n",
            "('size', 0.8887981063900303)\n",
            "('control_y', 0.30820941013104575)\n",
            "('control_x', 0.4436150901438063)\n",
            "('start_x', 0.8585130096303563)\n",
            "('start_y', 0.9840183957669343)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F7Ap5kRX/2bZDPPGDIEFi4AdBgsTAD4IECR8/2IIR6STn1Atqsq/tfXz264f6\n89VuA27lG+97vXy+djeF5Xpd9lytMlxMuZz79d7PZv/Zh/p41R0LbwJ27sTcDxfebJP8GbtctsOO\nVyVy6fFuJzjCKxRrzXPUQ10DPyuYOQdgDcCqqk6IyAiAHwI4CuAMgK+q6vRNXT0Igh3hZkz9X1HV\nB1R1Itt/GsBJVb0PwMlsPwiCPcB2TP3HATycbT+LSk29p7bZn2AH8Nl0vM9CGd4E5mBZlzP1OTuN\ns9ZGh6wu3QFaEOMTCPnabOZu0cQjU3zOaf+xQAib816DkEN4PozGzLlqv8vla5vb16hC8N2HBs1x\nXFn46PiQaTP3e62j6usA0ENekg85drTfnKlf7xNfAfyNiPxURI5nrx1U1QvZ9kUAB2/qykEQ7Bj1\nPvG/oKrnROQAgJdE5BfcqKoqIlUTvrM/FMcB4K677tpWZ4MgaAx1PfFV9Vz2/ySAH6NSHvuSiBwG\ngOz/yYL3nlDVCVWdGBsba0yvgyDYFjd84otIL4A2VZ3Ltr8M4D8CeAHAEwC+mf3/fDM7GjQW9qe9\nsMWaVg/n+eM4hNTjQmxcw49DeweGe81xHA4bHbKinyzmacNtNhQ3S751Z9k+y8rkC3Pve5yPv4/q\nB/a5kt+8GnBqesG0cRYtr/DzHjfXI/QiIEfvyH1+/pxtrmYC11DYsqJS2rPXURf1mPoHAfw461AH\ngP+uqn8lIq8B+JGIPAngAwBfre+SQRDsNDcc+Kr6HoBPVXn9CoBHm9GpIAiaS2TuJco6iVz4sJHS\nPpuO3ozkjLYFp6tfXs3NYy7XNTJozfm2GmW4Gc60u3D5umnjLDafXcjiHuzClEou/Eimv9fON3r5\nK+787DLRjhfbYBP+wIgNaXKtAXYR/L26766Rze22dZe5l90frbNYd+TqB0GCxMAPggSJgR8ECRI+\nfiL48I/13V1bwTnW3Io21pu/7lJZra+db/uS3APk1/vzF9Ws89r8HFJbckKcpgw3zTv4uQC+Bb4f\nFlerkOvxmbkGew6eo5iZWzZtHOrjeQJfq5BXMt5xoN+0bb6vznBePPGDIEFi4AdBgoSpnww+l6xW\nqenqOvhskgLAcjk3nVfK1sTmcJ7V97fPmuH+XKzSh8CmSdjy8ky+7c1o7uPZmmW48z55IQ6mllvk\nS2gNUf95hWKvy/7j47pdKJFFQPnaq6u2H+Ye15uiV0A88YMgQWLgB0GChKmfCjV03ut1A9bd4hhb\nEbf4ekI7vgwXi3QM1Mjc4xl+v1jo+mI+++1dCTaPOTLg6wyUa1TcrTVTPkZZeAdH84jF8EC3PW44\nP25k0Gbu8SIjvvRgv70f7C5Mn47nAAAH7ElEQVRs0di/Scn9eOIHQYLEwA+CBImBHwQJEj5+Knjd\nBvYJvbvIjaSo5gUeWQDT6+oX4UN2vO9FNFnMg7PYfChuhPzp/U7M89xkHt5jv95Hw5bJ//dzCKXO\n6qW2AaC3O+8jt3DfAevjD/VZ/5/nA3il4ZgTLeHVi34eYlOko05fP574QZAgMfCDIEHC1E+ELdEf\n84I1bbmFzWOfjcaln7yFWS7lbawV57PWBilE5ctO9ZOly6E4v8CGQ3F3HRowbeen5vL3UYbfshPb\nYO/Bnx+UrLelHDiFJzk0yaE3wN47r+nP7k4vaf/1dBWXCvNu14aLIHXa+vHED4IEiYEfBAkSAz8I\nEiR8/EQp8uMBYI1CeFwfyaeJchiqy/nu7HdzCMyvbmP/389D8HwAv8+vEuRad4ed0MfhsXyfQ4Kz\n8/ZiHMHrcHMN+8gH9/3nsBr73T4keHkm1+Ofd/X9ONWXQ3Y+dMgRPP+dbcw9bEmdLiCe+EGQIDHw\ngyBBwtRPFCG7UWqUxuIcOdlSJjvHh/oY1nr34SaTJOjs1KLS2D77j039fa4fd4zl2nQLi6xhV6zN\nv6U8NZn3PmOOyhOY45adO8LX7nQhQTbbOcOvVqak1x28Wep6t4gMicificgvRORtEfmciIyIyEsi\n8m72//C2ehIEQcuo98/GdwD8lap+HJVyWm8DeBrASVW9D8DJbD8Igj1APdVyBwF8EcC/AABVXQGw\nIiKPA3g4O+xZAK8AeKoZnQwaDxus7U68Aiy4Yab/7WGqNaaQjftAboXUb+qzXl6R2Q/YjLmFJWti\nc3XeRWrzFXd7unJT3Gf1caadzy7k2XuW2jZlt2D1CdecK8FZiVyZF3a9kXMzmq+5dwzAFID/JiI/\nE5H/mpXLPqiqF7JjLqJSVTcIgj1APQO/A8BnAHxXVT8NYB7OrNeKBlPVP0EiclxETonIqampqe32\nNwiCBlDPwD8L4Kyqvprt/xkqfwguichhAMj+n6z2ZlU9oaoTqjoxNjbWiD4HQbBNbujjq+pFEflQ\nRD6mqu8AeBTAW9m/JwB8M/v/+ab2NGgaPtuLQ1tGr0N9tptW3QYA+HmDKufz1/Y+/jqVguYQWKfz\ns3l/Zm7JtM3M5Xr8rNPvz1HqzH1yr9vP3fLhvKJMOV+Gm+/OFjGSUh4GZG3+1fXiMlz1Cp8UUW8c\n/18D+IGIlAC8B+BfomIt/EhEngTwAYCvbqsnQRC0jLoGvqq+DmCiStOjje1OEAStIDL3gi2wyc0W\nuzhrXslw32Lq2wPpJLbJ6OBt6Uj1c3CmHmAr9fpQHIfHOITnF9HwOVcKRC4q27atSMfPuxKz13Pt\nfO8dcHiSXQl/S3nfL9K5WSJXPwgSJAZ+ECRIDPwgSJDw8YOasL9f2610oT6zwzX2ik/i/e4iOpyf\nzavYtqzwo7DXQG8uHLJStnMBLO5h0mZh/f/uLicWSsIZXBdwjurhAfaz+U85v5TPUZj5FfdZ2P8X\nX9/vJoknfhAkSAz8IEgQqRmGafTFRKZQSfbZD+Byyy5cnd3QByD64Yl+WG62H3er6g1z41s68Dcv\nKnJKVaslBCXVh+hH9GOn+hGmfhAkSAz8IEiQnRr4J3bousxu6AMQ/fBEPyxN6ceO+PhBEOwsYeoH\nQYK0dOCLyGMi8o6InBaRlqnyisj3RWRSRN6g11ouDy4id4rIyyLyloi8KSLf2Im+iEi3iPxERH6e\n9eMPstePicir2ffzw0x/oemISHum5/jiTvVDRM6IyN+JyOsicip7bSd+Iy2Rsm/ZwBeRdgD/BcA/\nBXA/gK+JyP0tuvwfA3jMvbYT8uCrAH5XVe8H8BCAr2f3oNV9WQbwiKp+CsADAB4TkYcAfAvAt1X1\nXgDTAJ5scj82+AYqku0b7FQ/fkVVH6Dw2U78RlojZa+qLfkH4HMA/pr2nwHwTAuvfxTAG7T/DoDD\n2fZhAO+0qi/Uh+cBfGkn+4KKiPP/A/BZVBJFOqp9X028/pHsx/wIgBdRSfrfiX6cAbDfvdbS7wXA\nIID3kc29NbMfrTT1xwF8SPtns9d2ih2VBxeRowA+DeDVnehLZl6/jopI6ksAfglgRlU3Vqu06vv5\nIwC/h7xa1+gO9UMB/I2I/FREjmevtfp7aZmUfUzuobY8eDMQkT4Afw7gd1R1dif6oqprqvoAKk/c\nBwF8vNnX9IjIrwOYVNWftvraVfiCqn4GFVf06yLyRW5s0feyLSn7m6GVA/8cgDtp/0j22k5Rlzx4\noxGRTlQG/Q9U9S92si8AoKozAF5GxaQeEpGNpdqt+H4+D+A3ROQMgOdQMfe/swP9gKqey/6fBPBj\nVP4Ytvp72ZaU/c3QyoH/GoD7shnbEoDfBPBCC6/veQEVWXCgRfLgUlls/T0Ab6vqH+5UX0RkTESG\nsu0eVOYZ3kblD8BXWtUPVX1GVY+o6lFUfg//S1V/u9X9EJFeEenf2AbwZQBvoMXfi6peBPChiHws\ne2lDyr7x/Wj2pImbpPg1AH+Pij/571t43T8BcAFAGZW/qk+i4kueBPAugP8JYKQF/fgCKmba3wJ4\nPfv3a63uC4BPAvhZ1o83APyH7PWPAPgJgNMA/hRAVwu/o4cBvLgT/ciu9/Ps35sbv80d+o08AOBU\n9t38DwDDzehHZO4FQYLE5F4QJEgM/CBIkBj4QZAgMfCDIEFi4AdBgsTAD4IEiYEfBAkSAz8IEuT/\nA2eFivl22TpFAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 1] ac: [0.36515062 0.30124581 0.47204964 0.65072222 0.65151745 0.58561212\n",
            " 0.94529997 0.07012821 0.39330104 0.09341873 0.13907208 0.57115726]\n",
            "('end_y', 0.5856121174382521)\n",
            "('end_x', 0.6515174493639371)\n",
            "('color_g', 0.07012821322132035)\n",
            "('color_b', 0.39330104113802933)\n",
            "('pressure', 0.3651506189444672)\n",
            "('entry_pressure', 0.5711572595887029)\n",
            "('color_r', 0.9452999666942634)\n",
            "('size', 0.30124580694182057)\n",
            "('control_y', 0.6507222235396223)\n",
            "('control_x', 0.4720496399596482)\n",
            "('start_x', 0.09341872594776623)\n",
            "('start_y', 0.1390720766097302)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nf/hkuN+fvS58Ki9g+ZC7NFlY6X4fViyG7WjiTUUfVLX+xn4A4BsAzv21zAFwyszOfQKH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\nQgp8kQgp8EUipMAXiZACXyRC/w+zAayOzgOVTQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 2] ac: [0.09476984 0.23737978 0.45335428 0.42428263 0.4455534  0.65612943\n",
            " 0.19244729 0.01741144 0.76810654 0.01604109 0.57286472 0.10633994]\n",
            "('end_y', 0.6561294325314068)\n",
            "('end_x', 0.44555340086482953)\n",
            "('color_g', 0.017411440663484545)\n",
            "('color_b', 0.768106540693279)\n",
            "('pressure', 0.09476983659940319)\n",
            "('entry_pressure', 0.10633994436518801)\n",
            "('color_r', 0.19244729181189735)\n",
            "('size', 0.23737978067958754)\n",
            "('control_y', 0.42428262690577456)\n",
            "('control_x', 0.45335428471462236)\n",
            "('start_x', 0.016041085908013786)\n",
            "('start_y', 0.5728647197065228)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YD7wn6TPZphXAm8DzwKps2ypgfVsqNLPK9TZ53F8BP5DUB+wE/oL6k8azklYDu4H721Oi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\nfLMEOfhmCfp/GU+IMZ4AsPEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 3] ac: [0.56323853 0.60769168 0.24782252 0.95826642 0.35986308 0.21879381\n",
            " 0.3866676  0.53685841 0.00205911 0.71798041 0.34658607 0.87065781]\n",
            "('end_y', 0.21879380981931118)\n",
            "('end_x', 0.3598630810590875)\n",
            "('color_g', 0.5368584072771373)\n",
            "('color_b', 0.002059114656629779)\n",
            "('pressure', 0.5632385291365636)\n",
            "('entry_pressure', 0.8706578070325454)\n",
            "('color_r', 0.3866676046596915)\n",
            "('size', 0.6076916789220893)\n",
            "('control_y', 0.9582664209477781)\n",
            "('control_x', 0.24782251608811356)\n",
            "('start_x', 0.7179804087376247)\n",
            "('start_y', 0.34658606848380813)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rAJ4RkYcBHAXwtdJN03Gc9WTVhR9COAzglmWeHwVwfykm5ThOaanwyD2r5R63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\nFgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 4] ac: [0.46326046 0.68938223 0.81022043 0.31715687 0.56169805 0.53509667\n",
            " 0.57993134 0.47325214 0.32227619 0.00099051 0.53090457 0.7133904 ]\n",
            "('end_y', 0.5350966703563642)\n",
            "('end_x', 0.5616980510440263)\n",
            "('color_g', 0.4732521440564966)\n",
            "('color_b', 0.3222761863805066)\n",
            "('pressure', 0.46326046078452465)\n",
            "('entry_pressure', 0.7133903961725964)\n",
            "('color_r', 0.5799313384080698)\n",
            "('size', 0.6893822342459711)\n",
            "('control_y', 0.3171568685266385)\n",
            "('control_x', 0.8102204263584482)\n",
            "('start_x', 0.0009905088440437249)\n",
            "('start_y', 0.5309045650884464)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T0fl0P35KcRt7cKJJszC/+Kh+/F5xFOXW6/402XaWRu/ZrBgKphWbGbF4ysWiN74QGSLB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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Hd4jqVXg1q3L",
        "colab_type": "code",
        "outputId": "290cb5a8-1581-493c-8d79-173ee2187b81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2537
        }
      },
      "cell_type": "code",
      "source": [
        "env=ColorEnv(args, paint_mode=PaintMode.CONNECTED_STROKES)\n",
        "\n",
        "try:\n",
        "  os.makedirs('data/generated')\n",
        "except OSError:\n",
        "  pass\n",
        "\n",
        "for ep_idx in range(1):\n",
        "    env.reset()\n",
        "\n",
        "    for i in range(5):\n",
        "        action = env.random_action()\n",
        "        #action[env.actions_to_idx['color_r']] = 0.50\n",
        "        #action[env.actions_to_idx['color_g']] = 0.2\n",
        "        #action[env.actions_to_idx['color_b']] = 0.3\n",
        "        action[env.actions_to_idx['entry_pressure']] = 0.\n",
        "        print(\"[Step {}] ac: {}\".format(i, action))\n",
        "        ColorEnv.pretty_print_action(action)\n",
        "        env.draw(action)\n",
        "        plt.imshow(env.image)\n",
        "        plt.show()\n",
        "        env.save_image(\"data/generated/mnist{}_{}.png\".format(ep_idx, i))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Step 0] ac: [0.32542663 0.37338011 0.36733832 0.48550354 0.23065611 0.23097348\n",
            " 0.45006508 0.21099824 0.16291906 0.24169058 0.72456516 0.        ]\n",
            "('end_y', 0.230973482733566)\n",
            "('end_x', 0.23065611341710412)\n",
            "('color_g', 0.21099823920283978)\n",
            "('color_b', 0.16291906446897064)\n",
            "('pressure', 0.325426627643262)\n",
            "('entry_pressure', 0.0)\n",
            "('color_r', 0.45006507593491396)\n",
            "('size', 0.3733801136177013)\n",
            "('control_y', 0.48550354234559234)\n",
            "('control_x', 0.36733832463080496)\n",
            "('start_x', 0.24169057786216064)\n",
            "('start_y', 0.7245651603918453)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+WYKc+GYJcuKbJciJb5YgJ75Zgpz4Zgly4pslyIlvliAnvlmCnPhmCXLimyXIiW+WICe+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\nb5ag/wPMgbHGJzFd4gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 1] ac: [0.03044385 0.43777396 0.35321483 0.34914334 0.26010034 0.87082733\n",
            " 0.23282752 0.05244238 0.01913243 0.34816328 0.41009838 0.        ]\n",
            "('end_y', 0.8708273330860063)\n",
            "('end_x', 0.26010034026881035)\n",
            "('color_g', 0.05244237768434523)\n",
            "('color_b', 0.0191324263207161)\n",
            "('pressure', 0.03044384524715349)\n",
            "('entry_pressure', 0.0)\n",
            "('color_r', 0.23282752039353372)\n",
            "('size', 0.43777396232244514)\n",
            "('control_y', 0.3491433391822223)\n",
            "('control_x', 0.3532148303589294)\n",
            "('start_x', 0.3481632764775672)\n",
            "('start_y', 0.4100983775675553)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lPCXUlFrSt0skQQp8kQSpq78A4ZLUTc0tYUW038Xx8bw8MX4xqgu7892r4px7Tc36OKQ+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\nkiAFvkiCFPgiCfo/BqKgpfQ7/XgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 2] ac: [0.00355761 0.99963391 0.76194057 0.83948911 0.66236439 0.80897455\n",
            " 0.32711967 0.9174523  0.16179189 0.1977505  0.13337123 0.        ]\n",
            "('end_y', 0.8089745475184195)\n",
            "('end_x', 0.6623643930583905)\n",
            "('color_g', 0.9174522979697065)\n",
            "('color_b', 0.16179188837154068)\n",
            "('pressure', 0.0035576058829480672)\n",
            "('entry_pressure', 0.0)\n",
            "('color_r', 0.3271196731498318)\n",
            "('size', 0.999633912948483)\n",
            "('control_y', 0.8394891101499349)\n",
            "('control_x', 0.7619405736245075)\n",
            "('start_x', 0.1977504994387269)\n",
            "('start_y', 0.13337123415109253)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9u4QIECm+EAEixRciQOTjLwRnCqjcWYnL3StWes0uOiG8Fy/4efXdKaXelf517hReOybU\nTPvFi/GFG+G3L/Z5uU92K5lumf06r9TxgPb7dgkhmo4UX4gAkam/ANxor3JHxa3w2l2anIzLU5OX\nvLpKV2LO9/X3e3XljuTjcE3UWmNy6eZl0fvnmeZFp8Ryzfs8+5tZZnqOTKn8h96Ls4zztQ8uXJMl\nSbu7AOrxhQgQKb4QASJTf5G4JuXc3KxXNz2VmPfdK3q9utXOzrcdqYU4HZ31Tf1ac3LhI+M1Jmqu\n2Vvs/nm3mHNrvdvlmOK5UjjvR9qcz7vKOXQ9slqPwHFNUpWe2Z6zuCfPvWk30189vhABIsUXIkCk\n+EIEiHz8BeD6ba5fPzs747Vzo/U6e3u8urWbNiZ1Pd3+A0oFp8AW4eOnr/Cn8/L8z8X5ppnTb6nb\nFfXX5/Km8Ly6YlN96dfsH/t1pbz+MeNx7Z7oo5DiRxtmngMwC2DGzIZIDgB4FMBWAEcA3GNmp5sj\nphCikSzE1P+Eme0ws8s7O94PYJ+ZbQewLzoWQlwBLMXUvxvAbVH5YVT31LtvifIsO7kmsGMqzjiJ\nOGbnfFPf/Tld0e/nx+91jksVP4GHeZFq/hRhjsDZeNNXOVF2NVX1zdQ8l8ByQtoWG/3nTeG5Zn/N\nXJzrEsz5dbmuhHMLb67PfzFlJp9TyfzPrMT6fWfNY1m3mHmm2RTt8Q3AL0k+S3J3dG6DmR2PyicA\nbKh/qRCi3Sja43/MzI6RXA/gSZK/dyvNzMjafgMAoh+K3QBwzTXXLElYIURjKNTjm9mx6P8ogJ+i\nuj32SZIbASD6P5px7R4zGzKzocHBwXpNhBAtZt4en+QKACUzOxeVPwngHwE8DmAngAej/3ubKehy\nkPY/3aM5x6+fnPT3znP3y+vo8sNy3dV5pQ7/d9f1T/OmngpP5+U2Kzp16F5ROL42syptF1qGH59X\nN0ffj5+zWaedX5d3fxc6fn0J5VRtsVWIzHfkM++2HBN/RUz9DQB+Gg1+dAD4dzP7OclnADxGcheA\nNwHc0zwxhRCNZF7FN7PDAD5Y5/zbAO5ohlBCiOYSZORe8ag43wibM2cLLcfUn5qa9NqxnFzXm5rO\nY8Ux71MW5SymEykcU99q7ePkfjXTSe4qNqcdipPl4NREu+VEp2W9izURhM57mhbSfb99kz3PnM+u\nm/PcJ5/a9zFh1nED0u1KGaso2ztuT7H6QgSJFF+IAJHiCxEgQfr4+WRPc1mGj39p2p/Oq6xIpux6\n1/kJNecqydTTFP1EnFmZamrCPwv67rlhuu5vPtN1GVNUNQ9w22WKWHPk1zivMxVt6yfbzF6B59ek\nfPyM9yBvSm1Buf+vJMfeQT2+EAEixRciQGTq15BtUnoRYqXsFWEdvUnOfXb5d58tJy5CrblZP+Fj\nzbRZ3vxYwYk019RnajValqmfnsryk0tmUzRRRjqRpecGeO9H6jLvdaZlzM587+Im2yin5lndunRS\nDm91XkGzvx0Sb6rHFyJApPhCBIhM/RTuIHBefopSOTEHe/v9XW9nS0kEXrniv8VuHoe5dLINb7Q+\nOzFEplA15Az/O0PoZCohiGdWu31D9lh42nzNTmhS/LUwQ/4FmcpZ16X8hZI3Q+G/H14ijpoFPO4t\ns2c52g31+EIEiBRfiACR4gsRIEH6+IW3cE5f5/i77pbWa9b5mYWmkUTkzZVS4Wiu656zv1pRv7jG\n381oWjNl5/ij6Skq31fN7hu8KcGCXm2uvAvfLqD2/um5Pu+eRV9zeqqvnFnnj8W4YxLt3ae2t3RC\niKYgxRciQII09XNzqDHb9nTNw0pHshCHlk7OkJiG6Sm7rLx6tc92zuecKWpil0rpiLaMiDNk54pP\nm/1po9c/qi9jXrRi7UspZvvnBsxlTrEVP/LfjxxXouAW2u2AenwhAkSKL0SASPGFCJBAffy8BBJz\nddsBqek8561L++Zlc3387OSPaZdwzguj9eb9UrB+O/jjBiWvLscHL+ybFvdhM0N28zJ2FNw+IO81\n57b1ptuyffXaVYg5RwXHENoN9fhCBIgUX4gACdTUd8lbcZZ3WfaWS65pW8pZ+ZamXH/f0RqzNFeu\nrMV0uRFt6cfVdzPypqRyc+znbAfmRS+md7/OuafXrvCKP/dZaZM97w550YvtbdJnUajHJ7ma5I9I\n/p7kAZIfITlA8kmSr0X/1zRbWCFEYyhq6n8HwM/N7AZUt9M6AOB+APvMbDuAfdGxEOIKoMhuuasA\nfBzAXwCAmU0BmCJ5N4DbomYPA3gawH3NELKZ5Oa9y1lEk59UrZTZKj9NdANGiLO8hexduAovbMmP\nIMypy3urcvOI5IzC139ULu7rzI0gzLtHTrvs5CPtR5Ee/zoAYwD+jeRzJP812i57g5kdj9qcQHVX\nXSHEFUARxe8A8CEA3zWzmwFMIGXWW3X0pm5fQ3I3yWGSw2NjY0uVVwjRAIoo/giAETPbHx3/CNUf\ngpMkNwJA9H+03sVmtsfMhsxsaHBwsF4TIUSLmdfHN7MTJI+SfJ+ZHQRwB4BXor+dAB6M/u9tqqRN\nIneKKi8aLScJpZskssaX9G65yMwTeZdlRKrlT40VS+aRd1V6lrLoVFzuYzNWCS5oi6usZ+XImz+2\nk3PPNvfrXYrO4/81gB+Q7ARwGMBfomotPEZyF4A3AdzTHBGFEI2mkOKb2fMAhupU3dFYcYQQrUCR\ne4uk6I6ytTvuZk9R+ffJ3i3Xv2zp01yLxV98U8z8boQ53BCTOvdtu3JM9sWiWH0hAkSKL0SASPGF\nCBD5+AtgMb5fTR72wtNcbshrOrR3aTIthKJhqCH4xe8m1OMLESBSfCEChHmJIRr+MHIM1WCfdQBO\ntezB9WkHGQDJkUZy+CxUjmvNbN7Y+JYqfvxQctjM6gUEBSWD5JAcyyWHTH0hAkSKL0SALJfi71mm\n57q0gwyA5EgjOXyaIsey+PhCiOVFpr4QAdJSxSd5F8mDJA+RbFlWXpLfIzlK8iXnXMvTg5O8muRT\nJF8h+TLJe5dDFpLdJH9D8oVIjq9H568juT/6fB6N8i80HZLlKJ/jE8slB8kjJF8k+TzJ4ejccnxH\nWpLKvmWKT7IM4F8A/CmAGwF8nuSNLXr89wHclTq3HOnBZwB8xcxuBHArgC9F70GrZbkE4HYz+yCA\nHQDuInkrgG8A+JaZbQNwGsCuJstxmXtRTdl+meWS4xNmtsOZPluO70hrUtmbWUv+AHwEwC+c4wcA\nPNDC528F8JJzfBDAxqi8EcDBVsniyLAXwJ3LKQuAXgC/BfBhVANFOup9Xk18/pboy3w7gCdQXaSw\nHHIcAbAuda6lnwuAVQDeQDT21kw5WmnqbwZw1Dkeic4tF8uaHpzkVgA3A9i/HLJE5vXzqCZJfRLA\n6wDGzWwmatKqz+fbAL4KxNsKr10mOQzAL0k+S3J3dK7Vn0vLUtlrcA/56cGbAck+AD8G8GUzO7sc\nspjZrJntQLXHvQXADc1+ZhqSnwYwambPtvrZdfiYmX0IVVf0SyQ/7la26HNZUir7hdBKxT8G4Grn\neEt0brkolB680ZCsoKr0PzCznyynLABgZuMAnkLVpF5N8vJS7VZ8Ph8F8BmSRwA8gqq5/51lkANm\ndiz6Pwrgp6j+GLb6c1lSKvuF0ErFfwbA9mjEthPA5wA83sLnp3kc1bTgQIvSg7O6GP8hAAfM7JvL\nJQvJQZKro3IPquMMB1D9Afhsq+QwswfMbIuZbUX1+/DfZvbFVstBcgXJlZfLAD4J4CW0+HMxsxMA\njpJ8X3Tqcir7xsvR7EGT1CDFpwC8iqo/+fctfO4PARwHMI3qr+ouVH3JfQBeA/BfAAZaIMfHUDXT\nfgfg+ejvU62WBcAHADwXyfESgH+Izr8HwG8AHALwHwC6WvgZ3QbgieWQI3reC9Hfy5e/m8v0HdkB\nYDj6bP4TwJpmyKHIPSECRIN7QgSIFF+IAJHiCxEgUnwhAkSKL0SASPGFCBApvhABIsUXIkD+H5mY\nJ11Q2qx7AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 3] ac: [0.20891175 0.92287791 0.72349815 0.296454   0.92919243 0.24524658\n",
            " 0.22189355 0.04993547 0.33627227 0.45439776 0.69748406 0.        ]\n",
            "('end_y', 0.24524658330318427)\n",
            "('end_x', 0.9291924255303122)\n",
            "('color_g', 0.04993547376523011)\n",
            "('color_b', 0.3362722725734334)\n",
            "('pressure', 0.20891174905536225)\n",
            "('entry_pressure', 0.0)\n",
            "('color_r', 0.2218935537794937)\n",
            "('size', 0.922877905665151)\n",
            "('control_y', 0.2964539985294652)\n",
            "('control_x', 0.7234981475387975)\n",
            "('start_x', 0.4543977553412989)\n",
            "('start_y', 0.6974840583596295)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PSIRQ1O8xNonGysqyq1tZTVerS0bcrAUBJWI91To9wA6hWck3Asnbiv71YBfclYzdckNR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\ncOITEiGc+IREyP8Hu8hgBI8XqYkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "[Step 4] ac: [0.83328842 0.98344846 0.33902685 0.89123158 0.52925477 0.91303524\n",
            " 0.24966657 0.2142516  0.83175996 0.96174141 0.73542546 0.        ]\n",
            "('end_y', 0.9130352411236383)\n",
            "('end_x', 0.5292547683514356)\n",
            "('color_g', 0.2142515993495543)\n",
            "('color_b', 0.8317599640955169)\n",
            "('pressure', 0.8332884207039201)\n",
            "('entry_pressure', 0.0)\n",
            "('color_r', 0.24966657442185525)\n",
            "('size', 0.9834484610593238)\n",
            "('control_y', 0.891231583965259)\n",
            "('control_x', 0.3390268529310515)\n",
            "('start_x', 0.9617414052565931)\n",
            "('start_y', 0.7354254600146377)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Sb+oPYrulz2YdmexGy71OuvqNurbFO6ANh6Sk6hqN2M7lSzEMuHLNugvJ/giv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\n+MB3nBTiA99xUogPfMdJIT7wHSeF/H8/VK55qrUdMQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oceF6vnA1q0D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QmmnGkLU1qv8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FG8PvckglJFO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MidgiI1rLe_h",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate action to brushstroke image examples\n",
        "\n",
        "This takes a long time. You might want to adjust the number of shards or change the Colab runtime to local or save directly to Google Drive to avoid losing progress.\n",
        "\n",
        "Alternatively, just skip this step and download the strokes I generated."
      ]
    },
    {
      "metadata": {
        "id": "3bSfbLaLjdHv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "env=ColorEnv(args, paint_mode=PaintMode.STROKES_ONLY)\n",
        "\n",
        "try:\n",
        "  os.makedirs('data')\n",
        "except OSError:\n",
        "  pass\n",
        "\n",
        "NUM_SHARDS = 80\n",
        "NUM_STROKES_PER_SHARD = 100000\n",
        "\n",
        "SHARD_NUM_OFFSET = 0\n",
        "\n",
        "for i in range(SHARD_NUM_OFFSET, SHARD_NUM_OFFSET+NUM_SHARDS):\n",
        "  print('shard', i)\n",
        "  actions = []\n",
        "  strokes = []\n",
        "  for idx in range(NUM_STROKES_PER_SHARD):\n",
        "    env.reset()\n",
        "    if idx % 2000 == 0: print(idx)\n",
        "    \n",
        "    action = env.random_action()\n",
        "    actions.append(action)\n",
        "    env.draw(action)\n",
        "    strokes.append(env.image[:, :, :3])\n",
        "  actions = np.array(actions, dtype=np.float)\n",
        "  strokes = np.array(strokes, dtype=np.uint8)\n",
        "  np.savez_compressed(\"data/episodes_{}.npz\".format(i), actions=actions, strokes=strokes)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EwhdhVkdM7ZV",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Test out the generated files"
      ]
    },
    {
      "metadata": {
        "id": "2z3CXRd1TRma",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "loaded = np.load('data/episodes_0.npz')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-3L2cE3djdaQ",
        "colab_type": "code",
        "outputId": "4c4f8095-812e-40b9-9d2e-fb9028db2696",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "print loaded['actions'].shape\n",
        "print loaded['strokes'].shape\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(100000, 12)\n",
            "(100000, 64, 64, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "uIWBkyd_jdV0",
        "colab_type": "code",
        "outputId": "b3f7e791-f05c-4ebe-87e3-8e4c3ad8c778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "w=args.screen_size\n",
        "h=args.screen_size\n",
        "fig=plt.figure(figsize=(30, 10))\n",
        "cols = 10\n",
        "\n",
        "smaller_arr = loaded['strokes'][:cols]\n",
        "\n",
        "for col in range(cols):\n",
        "    img = smaller_arr[col][:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(1, 20, col+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NGSFqFmEbafuWE4Zyp1dde7/9wDPfMNnd3ufLf/4KWzd3OH1unfe87wlW1pa+bY8w7z2v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\nJHII/P/yRfqRqtKl0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2160x720 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "l4NbuBaiF3eo",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "for ac in loaded['actions'][:cols]:\n",
        "  print('___')\n",
        "  ColorEnv.pretty_print_action(ac)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s7vyPqLbjdQm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "shl-Vn7tjdNd",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\n",
        "env=ColorEnv(args, paint_mode=PaintMode.JUMP_STROKES)\n",
        "#env2 = ColorEnv(args, paint_mode=PaintMode.JUMP_STROKES)\n",
        "\n",
        "try:\n",
        "  os.makedirs('data')\n",
        "except OSError:\n",
        "  pass\n",
        "\n",
        "NUM_STROKES = 200000\n",
        "NUM_SHARDS = 1\n",
        "NUM_STROKES_PER_SHARD = NUM_STROKES/NUM_SHARDS\n",
        "\n",
        "for i in range(NUM_SHARDS):\n",
        "  env.reset()\n",
        "  #env2.reset()\n",
        "  actions = []\n",
        "  strokes = []\n",
        "  #full_images = []\n",
        "  for idx in range(NUM_STROKES_PER_SHARD):\n",
        "    if idx % 2000 == 0: print(idx)\n",
        "    \n",
        "    action = env.random_action()\n",
        "    actions.append(action)\n",
        "    env.draw(action)\n",
        "    #env2.draw(action)\n",
        "    strokes.append(env.image[:, :, :3])\n",
        "    #full_images.append(env2.image[:, :, :3])\n",
        "  actions = np.array(actions, dtype=np.float)\n",
        "  strokes = np.array(strokes, dtype=np.uint8)\n",
        "  #full_images = np.array(full_images, dtype=np.uint8)\n",
        "  np.savez_compressed(\"data/full_strokes_{}.npz\".format(i), actions=actions, strokes=strokes)\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_D9ezTtHjdFq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "GIq_7RrCjdCW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9FDftk_6jdAk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Et17CN9Xjc-1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}